Scholars and historians have traditionally given names to different periods based on dominant characteristics of the time. The time we live in is often called the information age. Development of more and more sophisticated communication systems together with larger and more complex institutions has made it so. Businesses of all sorts and private individuals too, rely heavily on timely information to make decisions on an ongoing basis. Timely information is truly a most important commodity.
Much, if not most information is incremental rather than continuous, and systems for assimilating, analyzing, and reporting such information must allow for this characteristic. Such information occurs and is reported in small bites. Transaction information and transaction analysis is a good example, and of this sort of information, stock sales reports are typical, and will be used as an example in the present patent application.
In the case of stock reports, such as the transactions of the New York Stock Exchange, good and timely information can easily make a difference between success and failure in stock trading. The same is true in transaction analysis in many other areas of human endeavor. In stock trading as an example, individuals making private decisions and people making decisions for large organizations, have to have certain information for their decisions, and success may well depend on quickly noting specific events or trends in transactions. If an event or trend is missed altogether or only noticed with serious delay, the result can be catastrophic.
It is quite common today among companies and individuals as well to use computer technology to track and analyze such information, and many systems for doing this sort of analysis has been developed. Some, in the case of individuals, are designed to operate on personal computers, such as laptop and desktop PCs. In the case of bigger organizations systems may operate on large and more powerful computers. Regardless of the ability of the computer equipment, however, there are still drawbacks, based primarily on the fact that the data stream in many cases is simply enormous. In the case of stock transactions for example, people make decisions based on such as instant prices, price trends, volume of sales, volume trends, high and low sales in a fixed period, and the like. Different analysts, of course, use differing criteria. To appreciate the scope of the problem, one need only check the volume of transactions for a single trading day on the New York Stock Exchange. On Jul. 16, 1996, for example, the sales volume on the New York Stock Exchange was 422,903,290 shares in the 8-hour trading day. This is more than 14,684 shares traded per second. Shares, of course, are not traded one-share-at-a-time, but typically in blocks of 100 shares. So the number of transactions (blocks) in this particular case is over 100 transactions per second. To make meaningful analysis on the basis of such a massive transaction data stream is a prodigious process.
What is clearly needed is an analysis system that can quickly and efficiently track and analyze such a massive data stream, using equipment of a reasonable size, power, and cost, and provide summary information analyzed according to diverse sets of criteria to subscribers.